The positioning of persons and goods is presently often carried out by use of satellite navigation (e.g. GPS), which, when performed outside of buildings, can yield acceptable accuracies also in case of pedestrians. Within buildings, as well as in areas where the visible range of the sky is considerably blocked from view (e.g. urban canyons, shopping malls, inner courts, railroad stations with partial roofing), there are often massive disturbances caused by shading of the direct signal path from the satellite, or multi-path errors may occur. As a remedy in such situations, use is made in principle of two options—which can also be combined:
1) the use of further radio systems for positioning which allow for reception also within buildings (e.g. Wireless LAN, mobile radio, Ultra Wide Band—UWB),
2) the use of sensor technology adapted to obtain information on the movement of the pedestrian or another body (e.g. inertial sensor technology, odometry in robots, barometric altimeters, passive and active sensors/systems).
A combination of various systems, signals and sensors is referred to as a sensor fusion; it is a suitable method particularly for dynamic bodies (thus, e.g., pedestrians, robots) for connecting the systems to each other in an optimum manner. A further option for improving the accuracy is the use of information on the environment, such as e.g. building floor plans. In “Integration of Foot-Mounted Inertial Sensors into a Bayesian Location Estimation Framework”, Krach, Bernhard and Robertson, Patrick (2008), in WPNC 2008, 2008 Mar. 27, Hannover, and “Cascaded Estimation Architecture for Integration of Foot-Mounted Inertial Sensors”, Krach, Bernhard and Robertson, Patrick (2008), in PLANS 2008, 2008 May 5-2008 May 8, Monterey, Calif., USA, it is demonstrated that prior knowledge of building floor plans and the mere use of an inertial sensor worn on the shoe (Inertial Measurement Unit for all three spatial axes—IMU) is suited for an unambiguous positioning of a pedestrian in the building. This known method is an important basis for the present application and will be explained in greater detail hereunder:
The pedestrian wears, on his/her shoe, an IMU whose acceleration and rotation-rate signals will be processed in an Extended Kalman Filter (EKF) so as to estimate the position of the shoe—and thus of the person. In order to reduce the problem of the continuously increasing error (“drift”), use is made of so-called Zero Velocity Updates (ZUPTs) so that, in the phases when the foot is resting on the ground, the EKF can be set to zero velocity (so-called pseudo measurement). The resting phases of the foot can be determined in a quite reliable and simple manner by evaluating the acceleration and rotation rates of the IMU since each pedestrian step made by a human involves the occurrence of a characteristic pattern. The use of the EKF and of the ZUPT in this manner was introduced by Foxlin (see Pedestrian Tracking with Shoe-Mounted Inertial Sensors, Eric Foxlin, November/December 2005, published by the IEEE Computer Society 0272-1716/05 2005 IEEE). This method, however, has the disadvantage that the errors—increased by the drift—of the orientation about the vertical axis (“heading”) are only poorly observable by the ZUPT, thus causing an increasing inaccuracy primarily of the person's direction. This is also the case—even through to a lesser extent—for the length of path that has been covered. Further, this system, if used alone, will generally only allow for a relative positioning (i.e. in relation to a known starting point). As described above, according to the publications by Krach and Robertson, there could be used an additional hypothesis filter (hereunder also referred to as a particle filter) so as to determine, together with known maps, the position in space in that the “particles” (also referred to as “hypotheses” but hereunder being called “particle”) are moved according to the measured pedestrian step estimation of the EFK (which mathematically corresponds to the derivation of a new state of the particle from the proposal function of the particle filter), however, with a respectively different assumed deviation of pedestrian step direction and pedestrian step length. Thus, the hypotheses will “fathom” all possible deviations of the EKF pedestrian step length determination from the real sequence of the person's pedestrian steps. Such hypotheses, normally ranging within the known walls and obstacles, will—as one possibility—be “rewarded”, by a high likelihood, in the “update” or “prediction” part of the “particle filter” (PF) algorithm, and will be allowed to be continued in the next time-instant step of the PF algorithm. Those hypotheses which represent “pedestrian steps through walls” will either be directly eliminated or be penalized more less strongly. A disadvantage of this method resides in the requirement for existing knowledge on the walls (building floor plan).
Plans (building floor plans) of existing buildings (or maps of existing paths) are normally established by use of means from the field of surveying technology which are expensive and complex. Often, building floor plans from the construction or planning phase of a building exist as hardcopies; these would either have to be manually scanned (with subsequent post-editing) or be automatically scanned (digitized) and processed to make it possible to detect e.g. walls. This process is expensive and prone to errors. Further, it will not detect various local conditions within/on a building, although these could be useful in the positioning according to the above method because they restrict the possible movement of the hypotheses—e.g. obstacles such as larger pieces of furniture, exhibition objects, large plant arrangements, barriers, temporary or permanent stands, bars, seating arrangements, partition walls etc.
Building floor plans and route networks are of value also outside the application for the mere positioning: in the simulating, adapting and checking of escape routes, in the optimizing and planning of buildings, structures and resources (e.g. optimization of an airport or hospital by use of the knowledge about spatial movement of persons and goods), in interventions by public authorities (e.g. in the fight against terrorism, in the freeing of hostages or in covert investigations), in cases of emergency such as large fires, in mass events (e.g. for analyzing the flows of large masses of people), in routing systems, in logistics (e.g. storage, process optimization), and in electronic assistants such as e.g. electronic navigation systems or museum guides. For a large number of the enumerated applications, such plans are valuable especially if not only the walls are known but also the routes actually used by persons—while such routes can be determined also by pieces of furniture and by other obstacles.